Known techniques for up-scaling an input image use separate interpolation filters for the x- and y-directions. In FIG. 1 a schematic representation of a conventional up-scaling system for one spatial dimension x or y is depicted. After processing the first dimension (e.g. x-direction) with a separable scaler, a similar scaler is used for the second dimension. An up-scaling factor by a rational factor L/M can be achieved by first insertion of (L−1) zero samples between the original samples 10, followed by a low-pass filter 11 to get a smooth function. Thereafter decimation by a factor M is done by simply removing (M−1) samples between the outputted samples 12. The low-pass filter 11 should be designed for the larger value of L or M. Simple, but low quality scalers use bilinear interpolation, more advanced scalers bicubic interpolation or polyphase filters.
Conventional up-scaling methods with separable polyphase filters aim at a broad bandwidth up to the Nyquist frequency of the input image signal to preserve the resolution. The goal is to suppress all frequencies above the Nyquist frequency in order to eliminate the appearance of the original pixel block structure.
In addition to the classical approach with interpolator 10, low-pass filter 11, and decimator 12 in FIG. 1, non-linear methods can be used to increase the sharpness in the output images. A number of non-linear scalers have been described and compared (see Zhao et al.: “Making the best of legacy video on modern displays”, Journal of the Society for Information Display, Volume 15, Issue 1, pp. 49-60, January 2007). They are limited to a fixed scaling factor of two which makes the decimation step obsolete. High quality polyphase filters are computationally complex. Independent of their complexity, they can only approximate ideal characteristics. High-order filters approximating ideal low-pass filters have the additional disadvantage of introducing ringing along single lines or edges.
A remaining problem of spatial scaling is the appearance of jagged lines (staircases) with the original pixel structure. They usually appear after applying up-conversion techniques with separable filters, including commonly used bi-cubic interpolation.
Simple techniques introduce additional blurring in the output images. Peaking with linear filters can partially compensate the blurring, but strong peaking may also cause an unnatural image impression. Peaking also increase the sensitivity to noise, creating additional artifacts.
Non-linear scaling methods are typically based on the original pixel grid and therefore limited to natural scaling numbers (2, 3, 4, etc.). The scaling factor is typically fixed to two which limits the range of applications significantly. The various resolutions of TV standards (SD and HD), source and display resolutions in the PC domain and mobile devices cannot be scaled with satisfying results by a fixed scaling factor of two. Even scaling between to HD standards (1920×1080 and 1280×720) requires a scaling factor of 1.5.
Also, a non-linear scaler has been described which has similarities to the one of the invention disclosure (see Greenspan et al.: “Image enhancement by non-linear extrapolation in frequency space”, IEEE Transactions on Image Processing, Vol. 9, No. 6, pp. 1035-1048, June 2000). It includes a separable scaler by a factor of two in the main processing path and the non-linear detail extraction in a parallel path with the same scaling factor. This approach has the disadvantages of keeping parts of the original pixel structure (appearance of jagged lines) due to the separable filter in the main path. Also the other path suffers from the original pixel structure when applying the separable scaling of the high-pass filtered detail signal. In addition, the structure of the parallel path is not suitable to limit the creation of higher frequencies to the output Nyquist frequency. Therefore, newly created alias components will affect the output images.